チュートリアルmilvusで時間ベースのランキングを実装するCompatible with Milvus 2.6.x

多くの検索アプリケーションでは、コンテンツの新鮮さはその関連性と同じくらい重要です。ニュース記事、商品リスト、ソーシャルメディアへの投稿、研究論文などはすべて、意味的な関連性と最新性のバランスをとるランキングシステムから利益を得ています。このチュートリアルでは、Milvusでディケイランカーを使用して時間ベースのランキングを実装する方法を紹介します。

Milvusのディケイランカーを理解する

ディケイランカーを使用すると、基準点からの相対的な数値（タイムスタンプのような）に基づいて文書をブーストしたりペナルティを与えたりすることができます。時間ベースのランキングでは、意味的な関連性が似ていても、新しい文書が古い文書より高いスコアを得ることができます。

Milvusは3種類のディケイランカーをサポートしています：

• ガウシアン(gauss)：滑らかで緩やかな減衰を提供するベル型の曲線。

• 指数(exp)：最近のコンテンツを強く強調するために、最初の減衰をよりシャープにします。

• Linear(linear)：予測可能でわかりやすい直線的な減衰。

それぞれのランカーは、さまざまなユースケースに適した異なる特性を持っています。詳しくは、ディケイ・ランカーの概要を参照してください。

時間を意識した検索システムの構築

関連性と時間の両方に基づいてコンテンツを効果的にランク付けする方法を示すニュース記事検索システムを作成します。まずは実装から始めよう：

import datetime
import matplotlib.pyplot as plt
import numpy as np
from pymilvus import (
    MilvusClient,
    DataType,
    Function,
    FunctionType,
    AnnSearchRequest,
)

# Create connection to Milvus
milvus_client = MilvusClient("http://localhost:19530")

# Define collection name
collection_name = "news_articles_tutorial"

# Clean up any existing collection with the same name
milvus_client.drop_collection(collection_name)

ステップ1：スキーマの設計

時間ベースの検索のためには、コンテンツと一緒に公開タイムスタンプを保存する必要がある：

# Create schema with fields for content and temporal information
schema = milvus_client.create_schema(enable_dynamic_field=False, auto_id=True)
schema.add_field("id", DataType.INT64, is_primary=True)
schema.add_field("headline", DataType.VARCHAR, max_length=200, enable_analyzer=True)
schema.add_field("content", DataType.VARCHAR, max_length=2000, enable_analyzer=True)
schema.add_field("dense", DataType.FLOAT_VECTOR, dim=1024)  # For dense embeddings
schema.add_field("sparse_vector", DataType.SPARSE_FLOAT_VECTOR)  # For sparse (BM25) search
schema.add_field("publish_date", DataType.INT64)  # Timestamp for decay ranking

ステップ2：埋め込み関数の設定

ここでは、密（セマンティック）埋め込み関数と疎（キーワード）埋め込み関数の両方を設定する：

# Create embedding function for semantic search
text_embedding_function = Function(
    name="siliconflow_embedding",
    function_type=FunctionType.TEXTEMBEDDING,
    input_field_names=["content"],
    output_field_names=["dense"],
    params={
        "provider": "siliconflow",
        "model_name": "BAAI/bge-large-en-v1.5",
        "credential": "your-api-key"
    }
)
schema.add_function(text_embedding_function)

# Create BM25 function for keyword search
bm25_function = Function(
    name="bm25",
    input_field_names=["content"],
    output_field_names=["sparse_vector"],
    function_type=FunctionType.BM25,
)
schema.add_function(bm25_function)

Milvus埋め込み関数の使い方の詳細については、埋め込み関数の概要を参照してください。

ステップ 3: インデックスパラメータの設定

高速ベクトル検索に必要なインデックスパラメータを設定します：

# Set up indexes for fast search
index_params = milvus_client.prepare_index_params()

# Dense vector index
index_params.add_index(field_name="dense", index_type="AUTOINDEX", metric_type="L2")

# Sparse vector index
index_params.add_index(
    field_name="sparse_vector",
    index_name="sparse_inverted_index",
    index_type="AUTOINDEX",
    metric_type="BM25",
)

# Create the collection with our schema and indexes
milvus_client.create_collection(
    collection_name,
    schema=schema,
    index_params=index_params,
    consistency_level="Bounded"
)

ステップ 4: サンプルデータの準備

このチュートリアルでは、発行日の異なるニュース記事のセットを作成します。ディケイランキング効果を明確に示すために、内容はほぼ同じだが日付が異なる記事のペアを入れたことに注目してください：

# Get current time
current_time = int(datetime.datetime.now().timestamp())
current_date = datetime.datetime.fromtimestamp(current_time)
print(f"Current time: {current_date.strftime('%Y-%m-%d %H:%M:%S')}")

# Sample news articles spanning different dates
articles = [
    {
        "headline": "AI Breakthrough Enables Medical Diagnosis Advancement",
        "content": "Researchers announced a major breakthrough in AI-based medical diagnostics, enabling faster and more accurate detection of rare diseases.",
        "publish_date": int((current_date - datetime.timedelta(days=120)).timestamp())  # ~4 months ago
    },
    {
        "headline": "Tech Giants Compete in New AI Race",
        "content": "Major technology companies are investing billions in a new race to develop the most advanced artificial intelligence systems.",
        "publish_date": int((current_date - datetime.timedelta(days=60)).timestamp())  # ~2 months ago
    },
    {
        "headline": "AI Ethics Guidelines Released by International Body",
        "content": "A consortium of international organizations has released new guidelines addressing ethical concerns in artificial intelligence development and deployment.",
        "publish_date": int((current_date - datetime.timedelta(days=30)).timestamp())  # 1 month ago
    },
    {
        "headline": "Latest Deep Learning Models Show Remarkable Progress",
        "content": "The newest generation of deep learning models demonstrates unprecedented capabilities in language understanding and generation.",
        "publish_date": int((current_date - datetime.timedelta(days=15)).timestamp())  # 15 days ago
    },
    # Articles with identical content but different dates
    {
        "headline": "AI Research Advancements Published in January",
        "content": "Breakthrough research in artificial intelligence shows remarkable advancements in multiple domains.",
        "publish_date": int((current_date - datetime.timedelta(days=90)).timestamp())  # ~3 months ago
    },
    {
        "headline": "New AI Research Results Released This Week",
        "content": "Breakthrough research in artificial intelligence shows remarkable advancements in multiple domains.",
        "publish_date": int((current_date - datetime.timedelta(days=5)).timestamp())  # Very recent - 5 days ago
    },
    {
        "headline": "AI Development Updates Released Yesterday",
        "content": "Recent developments in artificial intelligence research are showing promising results across various applications.",
        "publish_date": int((current_date - datetime.timedelta(days=1)).timestamp())  # Just yesterday
    },
]

# Insert articles into the collection
milvus_client.insert(collection_name, articles)
print(f"Inserted {len(articles)} articles into the collection")

ステップ 5: 異なるディケイランカーを設定する

それでは、3つの異なるディケイランカーを作成し、それぞれの違いを際立たせるために異なるパラメータを設定しましょう：

# Use current time as reference point
print(f"Using current time as reference point")

# Create a Gaussian decay ranker
gaussian_ranker = Function(
    name="time_decay_gaussian",
    input_field_names=["publish_date"],
    function_type=FunctionType.RERANK,
    params={
        "reranker": "decay",
        "function": "gauss",           # Gaussian/bell curve decay
        "origin": current_time,        # Current time as reference point
        "offset": 7 * 24 * 60 * 60,    # One week (full relevance)
        "decay": 0.5,                  # Articles from two weeks ago have half relevance 
        "scale": 14 * 24 * 60 * 60     # Two weeks scale parameter
    }
)

# Create an exponential decay ranker with different parameters
exponential_ranker = Function(
    name="time_decay_exponential",
    input_field_names=["publish_date"],
    function_type=FunctionType.RERANK,
    params={
        "reranker": "decay",
        "function": "exp",             # Exponential decay
        "origin": current_time,        # Current time as reference point
        "offset": 3 * 24 * 60 * 60,    # Shorter offset (3 days vs 7 days)
        "decay": 0.3,                  # Steeper decay (0.3 vs 0.5) 
        "scale": 10 * 24 * 60 * 60     # Different scale (10 days vs 14 days)
    }
)

# Create a linear decay ranker
linear_ranker = Function(
    name="time_decay_linear",
    input_field_names=["publish_date"],
    function_type=FunctionType.RERANK,
    params={
        "reranker": "decay",
        "function": "linear",          # Linear decay
        "origin": current_time,        # Current time as reference point
        "offset": 7 * 24 * 60 * 60,    # One week (full relevance)
        "decay": 0.5,                  # Articles from two weeks ago have half relevance
        "scale": 14 * 24 * 60 * 60     # Two weeks scale parameter
    }
)

先のコードでは

• reranker:時間ベースのディケイ機能にはdecay を設定

• function:減衰関数のタイプ（gauss、exp、linear）。

• origin:基準点（通常は現在時刻）

• offset:文書が完全な関連性を維持する期間

• scale:オフセットを超えて関連性が低下する速さを制御します。

• decay:オフセット＋スケールでの減衰係数（例えば、0.5は関連性が半分になることを意味する）

指数ランカーをさまざまなパラメータで設定し、これらの関数をどのように調整すればさまざまな動作ができるかを示していることに注目してください。

ステップ 6: ディケイランカーを視覚化する

検索を実行する前に、これらの異なる構成のディケイランカーがどのように動作するかを視覚的に比較してみましょう：

# Visualize the decay functions with different parameters
days = np.linspace(0, 90, 100)
# Gaussian: offset=7, scale=14, decay=0.5
gaussian_values = [1.0 if d <= 7 else (0.5 ** ((d - 7) / 14)) for d in days]
# Exponential: offset=3, scale=10, decay=0.3
exponential_values = [1.0 if d <= 3 else (0.3 ** ((d - 3) / 10)) for d in days]
# Linear: offset=7, scale=14, decay=0.5
linear_values = [1.0 if d <= 7 else max(0, 1.0 - ((d - 7) / 14) * 0.5) for d in days]

plt.figure(figsize=(10, 6))
plt.plot(days, gaussian_values, label='Gaussian (offset=7, scale=14, decay=0.5)')
plt.plot(days, exponential_values, label='Exponential (offset=3, scale=10, decay=0.3)')
plt.plot(days, linear_values, label='Linear (offset=7, scale=14, decay=0.5)')
plt.axhline(y=0.5, color='gray', linestyle='--', alpha=0.5, label='Half relevance')
plt.xlabel('Days ago')
plt.ylabel('Relevance factor')
plt.title('Decay Functions Comparison')
plt.legend()
plt.grid(True)
plt.savefig('decay_functions.png')
plt.close()

# Print numerical representation
print("\n=== TIME DECAY EFFECT VISUALIZATION ===")
print("Days ago | Gaussian | Exponential | Linear")
print("-----------------------------------------")
for days in [0, 3, 7, 10, 14, 21, 30, 60, 90]:
    # Calculate decay factors based on the parameters in our rankers
    gaussian_decay = 1.0 if days <= 7 else (0.5 ** ((days - 7) / 14))
    exponential_decay = 1.0 if days <= 3 else (0.3 ** ((days - 3) / 10))
    linear_decay = 1.0 if days <= 7 else max(0, 1.0 - ((days - 7) / 14) * 0.5)
    
    print(f"{days:2d} days | {gaussian_decay:.4f}   | {exponential_decay:.4f}     | {linear_decay:.4f}")

期待される出力

=== TIME DECAY EFFECT VISUALIZATION ===
Days ago | Gaussian | Exponential | Linear
-----------------------------------------
 0 days | 1.0000   | 1.0000     | 1.0000
 3 days | 1.0000   | 1.0000     | 1.0000
 7 days | 1.0000   | 0.6178     | 1.0000
10 days | 0.8620   | 0.4305     | 0.8929
14 days | 0.7071   | 0.2660     | 0.7500
21 days | 0.5000   | 0.1145     | 0.5000
30 days | 0.3202   | 0.0387     | 0.1786
60 days | 0.0725   | 0.0010     | 0.0000
90 days | 0.0164   | 0.0000     | 0.0000

ステップ 7: 結果表示用ヘルパー関数

# Helper function to format search results with dates and scores
def print_search_results(results, title):
    print(f"\n=== {title} ===")
    for i, hit in enumerate(results[0]):
        publish_date = datetime.datetime.fromtimestamp(hit.get('publish_date'))
        days_from_now = (current_time - hit.get('publish_date')) / (24 * 60 * 60)
        
        print(f"{i+1}. {hit.get('headline')}")
        print(f"   Published: {publish_date.strftime('%Y-%m-%d')} ({int(days_from_now)} days ago)")
        print(f"   Score: {hit.score:.4f}")
        print()

ステップ 8: 標準とディケイベースの検索を比較する

それでは、検索クエリを実行し、ディケイランキングの有無による結果を比較してみましょう：

# Define our search query
query = "artificial intelligence advancements"

# 1. Search without decay ranking (purely based on semantic relevance)
standard_results = milvus_client.search(
    collection_name,
    data=[query],
    anns_field="dense",
    limit=7,  # Get all our articles
    output_fields=["headline", "content", "publish_date"],
    consistency_level="Bounded"
)
print_search_results(standard_results, "SEARCH RESULTS WITHOUT DECAY RANKING")

# Store original scores for later comparison
original_scores = {}
for hit in standard_results[0]:
    original_scores[hit.get('headline')] = hit.score

# 2. Search with each decay function
# Gaussian decay
gaussian_results = milvus_client.search(
    collection_name,
    data=[query],
    anns_field="dense",
    limit=7,
    output_fields=["headline", "content", "publish_date"],
    ranker=gaussian_ranker,
    consistency_level="Bounded"
)
print_search_results(gaussian_results, "SEARCH RESULTS WITH GAUSSIAN DECAY RANKING")

# Exponential decay
exponential_results = milvus_client.search(
    collection_name,
    data=[query],
    anns_field="dense",
    limit=7,
    output_fields=["headline", "content", "publish_date"],
    ranker=exponential_ranker,
    consistency_level="Bounded"
)
print_search_results(exponential_results, "SEARCH RESULTS WITH EXPONENTIAL DECAY RANKING")

# Linear decay
linear_results = milvus_client.search(
    collection_name,
    data=[query],
    anns_field="dense",
    limit=7,
    output_fields=["headline", "content", "publish_date"],
    ranker=linear_ranker,
    consistency_level="Bounded"
)
print_search_results(linear_results, "SEARCH RESULTS WITH LINEAR DECAY RANKING")

期待される出力

=== SEARCH RESULTS WITHOUT DECAY RANKING ===
1. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.3670

2. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.4315

3. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.4316

4. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.6671

5. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.6674

6. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.7279

7. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.7661

=== SEARCH RESULTS WITH GAUSSIAN DECAY RANKING ===
1. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.5322

2. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.4316

3. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.3670

4. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.1180

5. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.0000

6. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0000

7. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.0000

=== SEARCH RESULTS WITH EXPONENTIAL DECAY RANKING ===
1. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.3670

2. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.3392

3. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.1574

4. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.0297

5. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.0007

6. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0000

7. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.0000

=== SEARCH RESULTS WITH LINEAR DECAY RANKING ===
1. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.4767

2. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.4316

3. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.3831

4. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.3670

5. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.3640

6. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.3335

7. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.2158

ステップ 9: スコア計算を理解する

元の関連性と減衰係数を組み合わせることで、最終的なスコアがどのように計算されるかを分解してみましょう：

# Add a detailed breakdown for the first 3 results from Gaussian decay
print("\n=== SCORE CALCULATION BREAKDOWN (GAUSSIAN DECAY) ===")
for item in gaussian_results[0][:3]:
    headline = item.get('headline')
    publish_date = datetime.datetime.fromtimestamp(item.get('publish_date'))
    days_ago = (current_time - item.get('publish_date')) / (24 * 60 * 60)
    
    # Get the original score
    original_score = original_scores.get(headline, 0)
    
    # Calculate decay factor
    decay_factor = 1.0 if days_ago <= 7 else (0.5 ** ((days_ago - 7) / 14))
    
    # Show breakdown
    print(f"Item: {headline}")
    print(f"  Published: {publish_date.strftime('%Y-%m-%d')} ({int(days_ago)} days ago)")
    print(f"  Original relevance score: {original_score:.4f}")
    print(f"  Decay factor (Gaussian): {decay_factor:.4f}")
    print(f"  Expected final score = Original × Decay: {original_score * decay_factor:.4f}")
    print(f"  Actual final score: {item.score:.4f}")
    print()

期待されるアウトプット

=== SCORE CALCULATION BREAKDOWN (GAUSSIAN DECAY) ===
Item: Latest Deep Learning Models Show Remarkable Progress
  Published: 2025-04-30 (15 days ago)
  Original relevance score: 0.6674
  Decay factor (Gaussian): 0.6730
  Expected final score = Original × Decay: 0.4491
  Actual final score: 0.5322

Item: New AI Research Results Released This Week
  Published: 2025-05-10 (5 days ago)
  Original relevance score: 0.4316
  Decay factor (Gaussian): 1.0000
  Expected final score = Original × Decay: 0.4316
  Actual final score: 0.4316

Item: AI Development Updates Released Yesterday
  Published: 2025-05-14 (1 days ago)
  Original relevance score: 0.3670
  Decay factor (Gaussian): 1.0000
  Expected final score = Original × Decay: 0.3670
  Actual final score: 0.3670

ステップ10：時間減衰を用いたハイブリッド検索

より複雑なシナリオでは、ハイブリッド検索を使って、密な（セマンティック）ベクトルと疎な（キーワード）ベクトルを組み合わせることができる：

# Set up hybrid search (combining dense and sparse vectors)
dense_search = AnnSearchRequest(
    data=[query],
    anns_field="dense",  # Search dense vectors
    param={},
    limit=7
)

sparse_search = AnnSearchRequest(
    data=[query],
    anns_field="sparse_vector",  # Search sparse vectors (BM25)
    param={},
    limit=7
)

# Execute hybrid search with each decay function
# Gaussian decay
hybrid_gaussian_results = milvus_client.hybrid_search(
    collection_name,
    [dense_search, sparse_search],
    ranker=gaussian_ranker,
    limit=7,
    output_fields=["headline", "content", "publish_date"]
)
print_search_results(hybrid_gaussian_results, "HYBRID SEARCH RESULTS WITH GAUSSIAN DECAY RANKING")

# Exponential decay
hybrid_exponential_results = milvus_client.hybrid_search(
    collection_name,
    [dense_search, sparse_search],
    ranker=exponential_ranker,
    limit=7,
    output_fields=["headline", "content", "publish_date"]
)
print_search_results(hybrid_exponential_results, "HYBRID SEARCH RESULTS WITH EXPONENTIAL DECAY RANKING")

期待される出力

=== HYBRID SEARCH RESULTS WITH GAUSSIAN DECAY RANKING ===
1. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 2.1467

2. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.7926

3. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.5322

4. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.1180

5. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.0000

6. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0000

7. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.0000

=== HYBRID SEARCH RESULTS WITH EXPONENTIAL DECAY RANKING ===
1. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 1.6873

2. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.7926

3. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.1574

4. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.0297

5. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.0007

6. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0001

7. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.0000

ステップ11：異なるパラメータ値での実験

スケールパラメータを調整することで、ガウス減衰関数にどのような影響を与えるか見てみましょう：

# Create variations of the Gaussian decay function with different scale parameters
print("\n=== PARAMETER VARIATION EXPERIMENT: SCALE ===")
for scale_days in [7, 14, 30]:
    scaled_ranker = Function(
        name=f"time_decay_gaussian_{scale_days}",
        input_field_names=["publish_date"],
        function_type=FunctionType.RERANK,
        params={
            "reranker": "decay",
            "function": "gauss",
            "origin": current_time,
            "offset": 7 * 24 * 60 * 60,  # Fixed offset of 7 days
            "decay": 0.5,                # Fixed decay of 0.5
            "scale": scale_days * 24 * 60 * 60  # Variable scale
        }
    )
    
    # Get results
    scale_results = milvus_client.search(
        collection_name,
        data=[query],
        anns_field="dense",
        limit=7,
        output_fields=["headline", "content", "publish_date"],
        ranker=scaled_ranker,
        consistency_level="Bounded"
    )
    
    print_search_results(scale_results, f"SEARCH WITH GAUSSIAN DECAY (SCALE = {scale_days} DAYS)")

期待される出力

=== PARAMETER VARIATION EXPERIMENT: SCALE ===

=== SEARCH WITH GAUSSIAN DECAY (SCALE = 7 DAYS) ===
1. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.4316

2. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.3670

3. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.2699

4. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.0004

5. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.0000

6. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0000

7. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.0000

=== SEARCH WITH GAUSSIAN DECAY (SCALE = 14 DAYS) ===
1. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.5322

2. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.4316

3. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.3670

4. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.1180

5. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.0000

6. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0000

7. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.0000

=== SEARCH WITH GAUSSIAN DECAY (SCALE = 30 DAYS) ===
1. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.6353

2. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.5097

3. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.4316

4. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.3670

5. Tech Giants Compete in New AI Race
   Published: 2025-03-16 (60 days ago)
   Score: 0.0767

6. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0021

7. AI Breakthrough Enables Medical Diagnosis Advancement
   Published: 2025-01-15 (120 days ago)
   Score: 0.0000

ステップ12：異なるクエリでのテスト

異なる検索クエリでのディケイランキングのパフォーマンスを見てみましょう：

# Try different queries with Gaussian decay
for test_query in ["machine learning", "neural networks", "ethics in AI"]:
    print(f"\n=== TESTING QUERY: '{test_query}' WITH GAUSSIAN DECAY ===")
    test_results = milvus_client.search(
        collection_name,
        data=[test_query],
        anns_field="dense",
        limit=4,
        output_fields=["headline", "content", "publish_date"],
        ranker=gaussian_ranker,
        consistency_level="Bounded"
    )
    print_search_results(test_results, f"TOP 4 RESULTS FOR '{test_query}'")

期待される出力

=== TESTING QUERY: 'machine learning' WITH GAUSSIAN DECAY ===

=== TOP 4 RESULTS FOR 'machine learning' ===
1. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.8208

2. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.7287

3. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.6633

4. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0000

=== TESTING QUERY: 'neural networks' WITH GAUSSIAN DECAY ===

=== TOP 4 RESULTS FOR 'neural networks' ===
1. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.8509

2. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.7574

3. Latest Deep Learning Models Show Remarkable Progress
   Published: 2025-04-30 (15 days ago)
   Score: 0.6364

4. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0000

=== TESTING QUERY: 'ethics in AI' WITH GAUSSIAN DECAY ===

=== TOP 4 RESULTS FOR 'ethics in AI' ===
1. New AI Research Results Released This Week
   Published: 2025-05-10 (5 days ago)
   Score: 0.7977

2. AI Development Updates Released Yesterday
   Published: 2025-05-14 (1 days ago)
   Score: 0.7322

3. AI Ethics Guidelines Released by International Body
   Published: 2025-04-15 (30 days ago)
   Score: 0.0814

4. AI Research Advancements Published in January
   Published: 2025-02-14 (90 days ago)
   Score: 0.0000

結論

Milvusのディケイ関数を使用した時間ベースのランキングは、セマンティックな関連性と最新性のバランスを取るための強力な方法を提供します。適切なディケイ関数とパラメータを設定することで、セマンティックな関連性を尊重しながらも、新鮮なコンテンツを強調する検索体験を作り出すことができます。

このアプローチは特に以下のような場合に有効です：

• ニュースやメディアプラットフォーム

• Eコマースの商品リスト

• ソーシャルメディアコンテンツフィード

• ナレッジベースとドキュメントシステム

• 研究論文のリポジトリ

減衰関数の背後にある数学を理解し、さまざまなパラメータを試すことによって、特定のユースケースに最適な関連性と新鮮さのバランスを提供するために検索システムを微調整することができます。